Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-22T20:43:15.512Z Has data issue: false hasContentIssue false
  • English
  • Français

Blending design of composite laminated structure with panel permutation sequence

Published online by Cambridge University Press:  04 January 2018

P. Jin ,
Y. Wang ,
X. Zhong ,
J. Yang  and
Z. Sun
P. Jin
Affiliation:
State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, Xi'an University of Technology, Xi'an, China
Y. Wang*
Affiliation:
State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, Xi'an University of Technology, Xi'an, China
X. Zhong
Affiliation:
School of Aeronautics, Northwestern Polytechnical University, Xi'an, China
J. Yang
Affiliation:
School of Aeronautics, Northwestern Polytechnical University, Xi'an, China
Z. Sun
Affiliation:
School of Aeronautics, Northwestern Polytechnical University, Xi'an, China
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Previously, the concept of Ply Drop Sequence (PDS) is introduced by the authors for the designing of composite laminated structures with multiple regions. Compared to deleting a contiguous innermost/outermost plies in the classical guide-based blending, using PDS is more flexible than dropping plies between adjacent regions. In this article, a new blending model called the Permutation for Panel Sequence (PPS) blending model is proposed to correct the problem of repeated searching of discrete points in the design space for the previous PDS blending model. The proposed method is also applied to an 18-panel horseshoe benchmark problem. The results demonstrate that the useful searching points in the PPS method are less than those in the PDS method when the number of the panels is less than the number of plies in the guide laminate, and the PPS method obtains a faster convergence speed compared with the PDS method.

Keywords

Composite Laminated StructureBlendingOptimisationGenetic Algorithm
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1248 , February 2018 , pp. 333 - 347
Copyright
Copyright © Royal Aeronautical Society 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Kristinsdottir, B.P. and Zabinsky, Z.B. Including manufacturing tolerances in composite design[C], 35th Structures, Structural Dynamics, and Materials Conference, 1994, Hilton head, SC, US.Google Scholar
2. Kristinsdottir, B.P. and Zabinsky, Z.B. Optimum design of large composite panels with varying loads[J], Composite Structures, 2001, 51, 93-102.Google Scholar
3. Kim, J.S., Kim, C.G. and Hong, C.S. Optimum design of composite structures with ply drop using genetic algorithm and expert system shell[J], Composite Structures, 1999, 46, (2), pp 171-187.CrossRefGoogle Scholar
4. Liu, B. and Haftka, R.T. Composite wing structural design optimization with continuity constraints. Proceedings of the 42nd AIAA/ASME/ASCE/AHA/ACS Structures, Structural Dynamics and Material Conference, 2001, Seattle, Washington, US.CrossRefGoogle Scholar
5. Toropov, V.V., Jones, R. and Willment, T. Weight and manufacturability optimization of composite aircraft components based on a genetic algorithm, 6th World Congresses of Structural and Multidisciplinary Optimization, 2005, Rio de Janeiro, Brazil.Google Scholar
6. Adams, D.B., Watson, L.T. and Gürdal, Z. Optimization and blending of composite laminates using genetic algorithms with migration[J], Mechanics of Advanced Materials and Structures, 2003, 10, pp 183-203.Google Scholar
7. Adams, D.B., Watson, L.T., Gürdal, Z. and Anderson-Cook, C.M. Genetic algorithm optimization and blending of composite laminates by locally reducing laminate thickness, Advances in Engineering Software, 2004, 35, pp 35-43.Google Scholar
8. Seresta, O., Abdalla, M.M. and Gürdal, Z. A genetic algorithm based blending scheme for design of multiple composite laminates, Proceedings of 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 4–7 May 2009, Palm Springs, California, US.Google Scholar
9. van Campen, J.M.J.F., Seresta, O., Abdalla, M.M. and Gürdal, Z. General blending definitions for stacking sequence design of composite laminate structures[C], 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Schaumburg, IL, 2008.Google Scholar
10. Zehnder, N. and Ermanni, P. A methodology for the global optimization of laminated composite structures[J], Composite Structures, 2006, 72, (3), pp 311-320.Google Scholar
11. Irisarri, F.X., Lasseigne, A., Leroy, F.H. and Riche, R.L. Optimal design of laminated composite structures with ply drops using stacking sequence tables[J], Composite Structures, 2014, 107, pp 559-569.Google Scholar
12. Zein, S., Basso, P. and Grihon, S. A primal-dual backtracking optimization method for blended composite structures[J], Struct Multidisc Optim, 2012, 45, pp 669-680.CrossRefGoogle Scholar
13. Zein, S., Basso, P. and Grihon, S. A constraint satisfaction programming approach for computing manufacturable stacking sequences[J]. Computers and Structures, 2014, 136, pp 56-63.Google Scholar
14. Jing, Z., Fan, X.L. and Sun, Q. Global shared-layer blending method for stacking sequence optimization design and blending of composite structures[J], Composites: Part B, 2015, 69, pp 181-190 Google Scholar
15. Jing, Z., Fan, X.L. and Sun, Q. Stacking sequence optimization of composite laminates for maximum buckling load using permutation search algorithm[J], Composite Structures, 2015, 121, pp 225-236 CrossRefGoogle Scholar
16. Fan, H.T., Wang, H. and Chen, X.H. An optimization method for composite structures with ply-drops[J]. Composite Structures, 2016, 136, pp 650-661.Google Scholar
17. Macquart, T. OPTIBLESS - An open-source toolbox for the optimisation of blended stacking sequence[C], ECCM17 - 17th European Conference on Composite Materials, 2016, Munich, Germany.Google Scholar
18. Macquart, T., Werter, N. and Breuker, R.D. Aeroelastic tailoring of blended composite structures using lamination parameters[C], 57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2016, San Diego, California, US.Google Scholar
19. Yang, J.B., Song, B.F., Zhong, X.P. and Jin, P. Optimal design of blended composite laminate structures using ply drop sequence[J], Composite Structures, 2016, 135, pp 30-37 Google Scholar
20. Deb, K. and Agrawal, R.B. Simulated binary crossover for continuous search space[J], Complex System, 1995, 9, (2), pp 115-148.Google Scholar
21. Davis, L. Applying adaptive algorithms to epistatic domains[C], Proceeding of 9th International Joint Conference on Artificial Intelligence, 1985, pp 162-164.Google Scholar
22. Gutin, G. and Karapetyan, D. A memetic algorithm for generalized travelling salesman problem[J]. Nat Comput, 2010, 9, pp 47-60.Google Scholar